Since the 1980s, the music video has increased in popularity and influence, and attracted greater audiences from a wide range of age groups. The style and content of music videos have strongly influenced advertising, television, film, and popular culture as a whole.
With ongoing technological advancements in multimedia content capture, storage, high bandwidth/speed transmission and compression standards, the production and distribution of music videos have increased rapidly and become more accessible to users. Nowadays, many music content providers provide users with the opportunity to purchase music videos though websites. It is useful to allow a customer to view highlights of a music video to assist in a purchasing decision. Such highlights may be referred to as a music video thumbnail. Thumbnails, enable a customer to be more informed, and more likely to make a correct purchase, thus increasing satisfaction and resulting in a greater likelihood of repeat purchase.
Thumbnails are available on music websites, and generally are generated manually. As the volume of music videos increases to meet the demands of consumers, the task of manually generating music video thumbnails becomes very labour-intensive and an inefficient use of time. Thus it is desirable to automatically create concise, accurate and informative thumbnails for original music videos.
Present efforts of automatic music summarisation may be classified into either machine learning-based approaches and pattern based approaches. Machine learning approaches attempt to categorize each frame of a song into groups based upon the distance between a particular frame and other frames in the song. The final thumbnail is generated based upon the group with the largest number of frames. Pattern matching approaches aim to categorize the frames based upon the content of those frames and select a pattern which is deemed to best match the required criteria. The challenge in music summarization is to determine the relevant features of the music and make the final summary correspond to meaningful sections of the music.
The known methods of video summarisation which have been successful in sport and movie video have not transferred well to music videos because the music signal, rather than the video track, is the dominant aspect.
It is a preferred object of the present inventing to overcome or at least reduce these shortcomings.